Wearable Multi-sensor Fusion Motion Capture System

In the process of human movement,the tracking of location information can help us obtain information about human health and environmental changes in the fields of military,production,commerce,transportation,games,movies,sports,medicine,etc.,help us make better plans,and give suggestions and judgments.As a result,it has a broad application prospect.With the development of science and technology,the time of human activities indoors has increased.Due to factors such as building occlusion,outdoor positioning systems cannot function.Therefore,research on indoor positioning technology is gradually becoming a mainstream hot spot.With the rise and development of Micro-Electro-Mechanical System(MEMS),the performance of Inertial Measurement Unit(IMU)has been greatly improved,and the volume and price are also greatly reduced.At the same time,wireless communication and data transmission technology is also developing significantly.All these sectors have provided a solid foundation for the birth and development of wearable multi-sensor fusion motion capture systems.Firstly,we build a hierarchical joint chain skeleton model by modeling human body.Then,we analyzed the gait cycle during human motion,divides the gait cycle into a support state and a swing state.Then we use inertial sensors to measure and calculate the acceleration,angular velocity,posture and other information of the human body during movement to track the position of the human body.This method has gradually become one of the mainstream methods for studying human body positioning.Due to the large noise and low accuracy of the MEMS inertial measurement sensor during operation,only using the IMU to collect human body motion information and integrate it will cause the accumulation of time,the error of the calculation result will gradually increase,and the reliability will be lost.Therefore,in the calculation process,we use the sensor output characteristics of the support state to propose a theoretical basis for the use of Zero Velocity Potential Update(ZUPT).The constraint conditions for the ZUPT need the threshold selection in the process for research,so we compared the advantages and disadvantages of single threshold detection method and double threshold detection method,propose an adaptive threshold detection method,use clustering method to classify experimental data,and obtain the threshold required for ZUPT.For the data collected by the inertial sensor during the human movement,the direct extended Kalman filter method and the indirect extended Kalman filter method are respectively used,and combined with the ZUPT for constraint correction.The human body motion position information is calculated,and the trace is reproduced.As a result,we revise the calculation results to ensure the reliability of the reproduction of human body position information.Experiments have proved that the indirect extended Kalman filter method using adaptive threshold detection method can track the position of the human body during the movement,and it can achieve a better recurring effect.